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      The “Infodemic” Infodemic: Toward a More Nuanced Understanding of Truth-Claims and the Need for (Not) Combatting Misinformation

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          Abstract

          Scholarship on (mis)information does not easily translate into recommendations for policy-makers and policy influencers who wish to judge the accuracy of science-related truth claims. This is partly due to much of this literature being based on lab experiments with captive audiences that tell us little about the durability or scalability of any potential intervention in the real world. More importantly, the “accuracy” of scientific truth claims is much more difficult to define than many scholars in this space acknowledge. Uncertainties associated with the nature of science, sociopolitical climates, and media systems introduce compounding error in assessments of claim accuracy. We, therefore, need a more nuanced understanding of misinformation and disinformation than those often present in discussions of the “infodemic.” Here, we propose a new framework for evaluating science-related truth claims and apply it to real-world examples. We conclude by discussing implications for research and action on (mis)information, given that distinguishing between true and false claims is not as easy as it is sometimes purported to be.

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          Most cited references28

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          Fake news on Twitter during the 2016 U.S. presidential election

          The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.
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            Defining “Fake News”

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              Science audiences, misinformation, and fake news

              Concerns about public misinformation in the United States—ranging from politics to science—are growing. Here, we provide an overview of how and why citizens become (and sometimes remain) misinformed about science. Our discussion focuses specifically on misinformation among individual citizens. However, it is impossible to understand individual information processing and acceptance without taking into account social networks, information ecologies, and other macro-level variables that provide important social context. Specifically, we show how being misinformed is a function of a person’s ability and motivation to spot falsehoods, but also of other group-level and societal factors that increase the chances of citizens to be exposed to correct(ive) information. We conclude by discussing a number of research areas—some of which echo themes of the 2017 National Academies of Sciences, Engineering, and Medicine’s Communicating Science Effectively report—that will be particularly important for our future understanding of misinformation, specifically a systems approach to the problem of misinformation, the need for more systematic analyses of science communication in new media environments, and a (re)focusing on traditionally underserved audiences.
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                Author and article information

                Journal
                The ANNALS of the American Academy of Political and Social Science
                The ANNALS of the American Academy of Political and Social Science
                SAGE Publications
                0002-7162
                1552-3349
                March 2022
                May 05 2022
                March 2022
                : 700
                : 1
                : 112-123
                Article
                10.1177/00027162221086263
                5e125cd7-5ef5-4329-9019-f2c1664ae8d2
                © 2022

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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